A tool for filtering information in complex systems

Date

2005

Authors

Tumminello, M
Aste, Tomaso
Di Matteo, Tiziana
Mantegna, R N

Journal Title

Journal ISSN

Volume Title

Publisher

National Academy of Sciences (USA)

Abstract

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph, We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant: relationships with the market structure and properties.

Description

Keywords

Keywords: algorithm; article; cluster analysis; correlation analysis; mathematical analysis; mathematical model; priority journal Cluster analysis; Complex networks; Correlation analysis

Citation

Source

PNAS - Proceedings of the National Academy of Sciences of the United States of America

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

DOI

10.1073/pnas.0500298102

Restricted until

2037-12-31